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A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control.

van Dyk E, Reinders MJ, Wessels LF - Nucleic Acids Res. (2013)

Bottom Line: The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization.An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales.Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Statistics group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

ABSTRACT
Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

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Related in: MedlinePlus

Illustrating the recursive multi-level detection methodology. (A) On recursive level 1, we detect recurrent aberrations with the proposed multi-scale methodology. Note that the region in which we finally estimate the  parameters ( and r) is restricted to , as illustrated by the dotted line at the top of the figure. (B) On recursive level 2, we follow the exact same procedure, except this time, estimate the  parameters in the broad event . This allows us to detect embedded focal events inside broader events.
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gkt155-F3: Illustrating the recursive multi-level detection methodology. (A) On recursive level 1, we detect recurrent aberrations with the proposed multi-scale methodology. Note that the region in which we finally estimate the parameters ( and r) is restricted to , as illustrated by the dotted line at the top of the figure. (B) On recursive level 2, we follow the exact same procedure, except this time, estimate the parameters in the broad event . This allows us to detect embedded focal events inside broader events.

Mentions: The events detected by the procedure as described thus far include focal and broad events, but we are not yet able to detect focal events that are embedded in broad events. To find those, we propose a recursive scheme that finds new events that are embedded in earlier detected events. For example, lets say that we find (among other) one broad recurrent gain that starts (ends) at genomic location . We re-estimate the parameters and r from all probes between and and perform the multi-scale analysis to find smaller events embedded within this broad event. This procedure for finding a focal event within a broad event is illustrated in Figure 3. Again we iteratively update the parameters until the region converges (a new region inside the broad event). Note that the boundaries of the detected broad event ( and ) might be inaccurate and therefore embedded focal events might be detected at the border of the initial broad event. As these are a result of the boundary inaccuracy, we simply ignore them (unless, e.g. it is a focal gain within a gain). We repeat this recursive procedure until no more events can be found and represent the results in recursive levels.Figure 3.


A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control.

van Dyk E, Reinders MJ, Wessels LF - Nucleic Acids Res. (2013)

Illustrating the recursive multi-level detection methodology. (A) On recursive level 1, we detect recurrent aberrations with the proposed multi-scale methodology. Note that the region in which we finally estimate the  parameters ( and r) is restricted to , as illustrated by the dotted line at the top of the figure. (B) On recursive level 2, we follow the exact same procedure, except this time, estimate the  parameters in the broad event . This allows us to detect embedded focal events inside broader events.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643574&req=5

gkt155-F3: Illustrating the recursive multi-level detection methodology. (A) On recursive level 1, we detect recurrent aberrations with the proposed multi-scale methodology. Note that the region in which we finally estimate the parameters ( and r) is restricted to , as illustrated by the dotted line at the top of the figure. (B) On recursive level 2, we follow the exact same procedure, except this time, estimate the parameters in the broad event . This allows us to detect embedded focal events inside broader events.
Mentions: The events detected by the procedure as described thus far include focal and broad events, but we are not yet able to detect focal events that are embedded in broad events. To find those, we propose a recursive scheme that finds new events that are embedded in earlier detected events. For example, lets say that we find (among other) one broad recurrent gain that starts (ends) at genomic location . We re-estimate the parameters and r from all probes between and and perform the multi-scale analysis to find smaller events embedded within this broad event. This procedure for finding a focal event within a broad event is illustrated in Figure 3. Again we iteratively update the parameters until the region converges (a new region inside the broad event). Note that the boundaries of the detected broad event ( and ) might be inaccurate and therefore embedded focal events might be detected at the border of the initial broad event. As these are a result of the boundary inaccuracy, we simply ignore them (unless, e.g. it is a focal gain within a gain). We repeat this recursive procedure until no more events can be found and represent the results in recursive levels.Figure 3.

Bottom Line: The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization.An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales.Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Statistics group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

ABSTRACT
Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

Show MeSH
Related in: MedlinePlus